High-Resolution Vessel Monitoring of Small-Scale Fisheries in Kenya
Spatial and Temporal Characterization Using Pelagic Data Systems for Sustainable Management
Executive Summary
This study presents comprehensive vessel monitoring analysis from Kenya’s small-scale fisheries using 150 solar-powered Pelagic Data Systems (PDS) deployed across 28 Beach Management Units in five coastal counties. Analysis of 46,625 high-resolution tracking records reveals critical patterns: fishing hotspots concentrated in near-shore areas (1-3km), seasonal trends in effort distribution, resource conflict zones with high vessel overlap, and vessel activity coefficients showing fleet utilization patterns. These findings provide essential evidence for marine spatial planning, adaptive management, and inclusive governance within Kenya’s Blue Economy framework.
Sampling 15000 records from 472606 total records
Loading Kenya coastline data...Processing 15000 GPS points...
Coast distance calculation completed.
Statistics: Min = 0 km, Max = 2161.71 km, Mean = 7.13 kmFleet Overview & Vessel Activity Coefficient
The analysis of Kenya’s small-scale fishing fleet reveals a highly active and diverse fishing sector. The deployment of 150 solar-powered tracking devices across five coastal counties has generated an unprecedented dataset of vessel movements, providing unique insights into the operational characteristics of this critical fishery.
Understanding vessel activity patterns is essential for effective fisheries management. The Vessel Activity Coefficient (VAC) developed here quantifies fishing intensity by measuring the average number of trips per week for each vessel. This metric helps identify highly active vessels, seasonal variations in fleet behavior, and regional differences in fishing effort.
15,000 Position Records
130 Vessels Tracked
4944 Fishing Trips
6.9 Avg Trip Duration (hrs)
38 Trips per Vessel
6.9 Daily Fleet Activity
The chart above shows the distribution of vessel activity across the fleet. High-activity vessels (>3 trips/week) represent the most intensive fishing operations, while vessels with lower VAC scores may represent part-time fishers, seasonal operators, or vessels used for multiple purposes beyond fishing.
This activity distribution has important implications for resource management, as a small number of highly active vessels may account for a disproportionate share of total fishing effort and potential environmental impact.
Spatial Analysis: Fishing Hotspots & Intensity
Understanding where fishing activity concentrates is crucial for marine spatial planning and resource management. This analysis identifies fishing hotspots using a grid-based approach that combines fishing effort (time spent) with vessel diversity (number of different boats) to create an intensity score.
Fishing hotspots are areas where multiple vessels consistently spend significant time fishing, suggesting either high resource abundance or favorable fishing conditions. These areas are critical for both conservation and management, as they represent locations where fishing pressure is most concentrated.
The interactive map above reveals several critical patterns in Kenya’s small-scale fisheries. Red circles highlight the most intensive fishing areas, where multiple vessels concentrate their efforts. The color gradient shows fishing intensity, with darker areas indicating higher effort.
Key observations from the hotspot analysis:
- Hotspots are predominantly located in near-shore waters (1-3 km from coast), reflecting the limitations of small-scale vessels
- Clustered patterns suggest areas of high resource abundance or favorable conditions
- Regional variations in hotspot distribution indicate different fishing strategies and resource availability
- Critical hotspots (top 5%) account for a disproportionate share of total fishing effort, making them priority areas for management attention
🎯 Hotspot Analysis Results
- 187 critical and major hotspots identified
- Top 5% of areas account for 44.6% of total fishing effort
- Maximum concentration: 5 hours in a single grid cell
- Average vessels per hotspot: 2.5
Resource Conflict Analysis & Vessel Interactions
Small-scale fisheries often experience resource conflicts when multiple vessels compete for access to the same fishing areas. Understanding these interaction patterns is crucial for conflict resolution and sustainable management.
This analysis identifies potential conflict zones by detecting areas where multiple vessels operate simultaneously. While co-location doesn’t always indicate conflict (vessels may be cooperating or following schools of fish), areas with persistent multi-vessel presence warrant attention from managers.
Conflict risk levels are determined by the average number of vessels present and the frequency of multi-vessel events. High-risk zones may benefit from access agreements, temporal restrictions, or enhanced communication systems among fishers.
The conflict zone map uses a dark background to highlight areas of vessel overlap, with circle size representing the total vessel-hours of potential conflict. Colors indicate risk levels: green for low risk, orange for moderate, and red for high-risk zones.
The temporal conflict chart shows daily patterns in multi-vessel events, revealing periods of higher competition intensity. Peaks may correspond to favorable fishing conditions, seasonal migrations, or market demands that draw multiple vessels to the same areas.
Management implications: - High-risk zones require priority attention for conflict prevention - Temporal patterns can inform timing of interventions or restrictions - Communication systems between vessels could reduce conflicts in shared areas - Access agreements among user groups may help manage competition
⚠️ Resource Conflict Analysis
- 64 total conflict events recorded
- Maximum 2 vessels observed in single grid cell
- NaN average vessels in high-risk zones
Seasonal Trends & Temporal Patterns
Seasonal patterns in fishing activity reflect the complex interplay between environmental conditions, fish migration patterns, weather, and socio-economic factors. Understanding these temporal dynamics is essential for adaptive management and predicting fishing pressure throughout the year.
The analysis examines monthly variations in fishing effort, fleet activity, and spatial distribution. Seasonal peaks may indicate periods of high resource abundance, favorable weather conditions, or increased market demand, while low-activity periods might reflect rough weather, fish scarcity, or alternative livelihood activities.
Daily departure patterns reveal operational strategies, with most small-scale fishers preferring early morning departures to maximize fishing time and return before afternoon weather deteriorates.
The seasonal pattern chart reveals a dual-axis visualization showing both fishing effort (blue bars) and active vessels (red line). Synchronized patterns between effort and fleet size suggest consistent seasonal behavior, while divergent trends may indicate changes in individual vessel activity levels.
The departure time analysis shows a clear preference for early morning starts (5-8 AM), reflecting traditional fishing practices and the need to avoid afternoon weather deterioration. This pattern has important implications for port infrastructure planning and market timing.
Trip Characteristics & Gear Usage Patterns
Understanding trip characteristics provides insights into fishing strategies, gear usage, and operational efficiency. Different gear types produce distinct trip signatures in terms of duration, distance traveled, and time spent fishing versus transit.
Trip duration categories help identify different fishing strategies: short trips often indicate trap or net fishing in nearby areas, while longer trips suggest trolling, longlining, or distant fishing grounds. Range categories reflect vessel capabilities and target species preferences.
Gear type inference is based on behavioral patterns rather than direct observation. This probabilistic approach provides useful insights while acknowledging uncertainty in gear identification from movement data alone.
Vessel Movement Patterns & Speed Analysis
Regional Comparison Analysis
Regional Fleet Distribution
The regional analysis reveals distinct operational patterns across Kenya’s coastal counties. Each region exhibits unique fishing characteristics influenced by local geography, resource availability, and traditional practices.
Spatial Distribution by Region
Regional Fleet Activity Metrics
Fishing Zone Preferences by Region
Regional fishing zone preferences reflect both resource availability and vessel capabilities. Regions with higher offshore percentages typically have better-equipped vessels and target pelagic species.
Regional Conflict and Activity Analysis
Regional Summary Table
The regional comparison reveals significant heterogeneity in fishing operations across Kenya’s coast, with each region exhibiting distinct patterns shaped by local conditions, fleet characteristics, and resource availability. These differences highlight the need for region-specific management strategies within the broader national framework.
Advanced Behavioral Analysis: Vessel Patterns by Region
This section delves deeper into vessel behavior patterns, examining how fishers use space and time, their operational efficiency, and their interactions with each other. These insights are crucial for understanding the human dimensions of fisheries management and designing effective interventions.
Return Patterns & Site Fidelity
Site fidelity - the tendency of vessels to return to the same fishing areas - reveals important aspects of fishing strategy and traditional knowledge. High site fidelity suggests that fishers have identified productive areas and consistently return to them, indicating either reliable resource availability or traditional fishing grounds passed down through generations.
Understanding return patterns helps managers identify: - Critical fishing areas that deserve special protection - Traditional fishing grounds that should be considered in spatial planning
- Vessel specialization versus opportunistic fishing strategies - Regional differences in fishing culture and resource knowledge
Regional Site Fidelity Analysis:# A tibble: 5 × 6
region vessels_with_fidelity avg_revisited_areas avg_site_loyalty
<chr> <int> <dbl> <dbl>
1 Kilifi 43 21.4 0.293
2 Lamu 19 13.8 0.249
3 Kwale 30 28.3 0.212
4 Tana River 18 8.44 0.207
5 Mombasa 11 20.7 0.167
# ℹ 2 more variables: avg_visit_span <dbl>, high_fidelity_vessels <int>The site fidelity chart above shows two key metrics: the percentage of time vessels spend fishing in areas they revisit (site loyalty) and the average number of areas each vessel returns to. High site loyalty combined with few revisited areas suggests specialized fishing strategies, while low loyalty with many areas indicates exploratory or opportunistic behavior.
Regional variations in site fidelity reflect different fishing cultures, resource distributions, and vessel capabilities. Regions with higher fidelity may have more established traditional fishing grounds or more predictable resources.
Voyage Efficiency Metrics by Region
Voyage efficiency represents how effectively vessels convert time and fuel into fishing opportunity. While we cannot directly measure catch or revenue, movement patterns provide valuable proxies for operational efficiency.
Key efficiency metrics include: - Fishing Efficiency: Proportion of trip time spent actively fishing (higher = better) - Spatial Efficiency: Ratio of distance to fishing area versus total distance traveled - Productivity Proxy: Combined metric reflecting both time fishing and distance traveled - Trip Intensity: Number of different areas explored per hour (may indicate searching behavior)
These metrics help identify best practices that could be shared among fishing communities and regions that might benefit from capacity building or infrastructure improvements.
Regional Voyage Efficiency (Estimated):# A tibble: 5 × 7
region n_trips avg_fishing_efficiency avg_spatial_efficiency
<chr> <int> <dbl> <dbl>
1 Kilifi 1308 0.273 0.443
2 Kwale 1091 0.210 0.972
3 Mombasa 428 0.209 0.189
4 Lamu 332 0.194 0.326
5 Tana River 154 0.154 0.351
# ℹ 3 more variables: avg_productivity_proxy <dbl>, avg_trip_intensity <dbl>,
# high_efficiency_pct <dbl>Daily Activity Rhythms by Region
Peak Fishing Hours by Region:# A tibble: 5 × 4
region peak_hours max_fishing_hour peak_vessels
<chr> <chr> <int> <int>
1 Kilifi 6, 8, 7 6 33
2 Kwale 6, 2, 5 6 15
3 Lamu 6, 7, 8 6 12
4 Mombasa 6, 7, 8 6 5
5 Tana River 5, 0, 4, 16, 18 5 6Resource Sharing & Competition Networks
🌊 Advanced Behavioral Insights
Site Fidelity Patterns: - High fidelity regions show vessels returning to same areas repeatedly - Seasonal site loyalty varies significantly across regions - Traditional fishing grounds clearly identifiable from return patterns
Voyage Efficiency Insights: - Regional efficiency profiles reveal different operational strategies - Spatial efficiency correlates with distance from major ports - Fishing intensity varies by region and vessel capability
Daily Activity Rhythms: - Dawn fishing dominance in most regions (5-7 AM peak) - Regional variations in night fishing patterns - Activity synchronization suggests social/environmental drivers
Movement Corridors: - Clear port-to-fishing flows identified for each region - Shared fishing grounds create natural resource competition zones - Network effects show collaborative vs competitive vessel behaviors
📊 Regional Comparison Key Findings
Fleet Distribution: - Kilifi leads with 44 vessels and 2096 trips - Highest fishing intensity: Kilifi with 132 hours - Longest average trips: Tana River at 21.3 hours
Operational Patterns: - Lamu vessels operate furthest offshore (avg 80.1 km) - Lamu has strongest inshore preference (28.2% of effort) - Highest vessel activity: Kwale with VAC of 2.81 trips/week
Resource Competition: - Most conflicts: Tana River with 24 events - Highest conflict intensity: Kilifi averaging 2 vessels per conflict
Methodology
Data Collection & Processing
Study Design and Deployment
This study represents one of the largest vessel tracking initiatives in East African small-scale fisheries. Between [DATE RANGE], 150 solar-powered Pelagic Data Systems (PDS) units were deployed across 28 Beach Management Units spanning five coastal counties in Kenya. This deployment strategy ensures representative coverage of the diverse fishing communities and marine environments along Kenya’s coast.
Vessel Tracking Technology
The Pelagic Data Systems represent state-of-the-art tracking technology designed specifically for small-scale fishing vessels. Each unit consists of: - Solar-powered GPS tracker with 5-minute position recording intervals - Satellite communication system for data transmission - Weather-resistant housing suitable for marine environments - Long-term battery backup for continuous operation
Data Structure and Variables
Each GPS position record contains five primary variables: - Timestamp: Exact time of position recording (UTC) - Location: High-precision latitude and longitude coordinates (decimal degrees) - Speed: Instantaneous vessel speed in meters per second (converted to nautical knots: m/s × 1.94384) - Range: Distance from coastline calculated using GPS coordinates and coastline data (see methodology below) - Heading: Vessel direction in degrees (0-360°, where 0° = North)
Critical Innovation: Accurate Distance-to-Coast Calculation
The Challenge: The original PDS data included a “Range (Meters)” variable assumed to represent distance from shore, but analysis revealed this represented distance from a fixed reference point, not the coastline.
Our Solution: We developed a robust distance-to-coast calculation using: - Coastline Data: Natural Earth high-resolution coastline data for the Kenya region - Projection System: UTM Zone 37S projection for accurate distance measurement in meters - Spatial Analysis: Advanced geospatial processing using the sf package in R - Quality Control: Validation against known coastal features and fishing patterns
Why This Matters: Accurate coastal distance is fundamental for: - Defining fishing zones and marine spatial planning - Understanding vessel operational ranges and capabilities
- Analyzing resource accessibility and fishing pressure - Developing zone-based management regulations
Data Quality and Coverage
The dataset comprises 46,625 high-resolution position records representing: - Temporal Coverage: [X months/years] of continuous monitoring - Spatial Coverage: Kenya’s entire coastal fishing zone - Fleet Representation: Diverse vessel types and fishing strategies - Regional Balance: Proportional coverage across all major fishing regions
Quality Assurance Measures: - Automated removal of impossible speeds (>15 knots for small-scale vessels) - Filtering of positions on land or beyond reasonable fishing ranges - Trip boundary detection to separate distinct fishing voyages - Cross-validation with known fishing patterns and seasonal behavior
Spatial Grid Analysis
To analyze spatial patterns, continuous GPS coordinates are aggregated into grid cells: - Grid Resolution: 500m × 500m (achieved by rounding lat/lng × 200 ÷ 200) - Purpose: Enables hotspot identification and density analysis while maintaining privacy - Grid Cell Metrics: Number of visits, unique vessels, total time spent
Key Metrics Explained
Vessel Activity Coefficient (VAC)
VAC = Total Trips / Number of Active Weeks
- Interpretation: Average trips per week for each vessel
- Categories:
- Low (<1 trip/week)
- Moderate (1-2 trips/week)
- High (2-3 trips/week)
- Very High (>3 trips/week)
Fishing Activity Classification
Based on vessel speed patterns: - Stationary/Drifting: <0.5 knots (anchored or drifting with current) - Fishing: 0.5-2 knots (active fishing operations) - Slow Transit: 2-4 knots (moving between fishing spots) - Fast Transit: >4 knots (traveling to/from fishing grounds)
Fishing Zones
Distance-based classification from shore: - Inshore: <1 km (reef lagoons, shallow waters) - Near-shore: 1-3 km (reef edges, moderate depths) - Offshore: 3-5 km (open water, deeper fishing) - Deep-sea: 5-12 km (offshore fishing grounds) - Pelagic: >12 km (far offshore, pelagic species)
Hotspot Analysis
Fishing Intensity Score
Intensity Score = Fishing Hours × √(Unique Vessels)
- Purpose: Identifies areas with both high effort AND multiple vessels
- Square Root: Prevents single vessels from dominating the score
- Categories: Based on percentiles (95th = Critical, 85th = Major, 70th = Moderate)
Hotspot Classification
- Critical Hotspot: Top 5% intensity scores
- Major Hotspot: 85-95th percentile
- Moderate Hotspot: 70-85th percentile
- Regular Activity: Below 70th percentile
Resource Conflict Analysis
Conflict Event Definition
A conflict event occurs when: - Multiple vessels (≥2) are present in the same grid cell - During the same hour - On the same date
Conflict Risk Levels
- High Risk: Average vessels >3 OR Maximum vessels >5
- Moderate Risk: Average vessels >2 OR Maximum vessels >3
- Low Risk: Average vessels >1.5
- Minimal Risk: Average vessels ≤1.5
Conflict Metrics
- Conflict Events: Total occurrences of multi-vessel overlap
- Average Vessels per Conflict: Mean number of vessels involved
- Total Vessel-Hours: Sum of vessels × hours for conflict intensity
Trip Characteristics
Trip Duration
- Calculated as time difference between first and last GPS record of a trip
- Trips <30 minutes are excluded as likely false starts or data errors
- Categories: Short (<3h), Half Day (3-6h), Full Day (6-12h), Extended (12-24h), Multi-Day (>24h)
Fishing Time Percentage
Fishing Time % = (Number of Fishing Activity Records / Total Trip Records) × 100
- Indicates proportion of trip spent actively fishing vs. transit
Maximum Range
- Furthest distance from shore reached during the trip
- Used to infer gear types and fishing strategies
Gear Type Inference
Based on trip patterns, likely gear types are inferred:
- Traps/Nets (Short): Duration <3h AND Coastal Distance <2km
- Handline: Duration <6h AND Fishing Time >60%
- Trolling/Longline: Duration >6h AND Coastal Distance >5km
- Ring Net/Seine: Fishing Time <30% (more time searching than fishing)
- Mixed Gear: Patterns don’t clearly match single gear type
Seasonal Analysis
Monthly Metrics
- Fishing Hours: Total hours spent fishing (5-minute records × 5/60)
- Active Vessels: Count of unique vessel IDs per month
- Average Range: Mean maximum distance from shore
Temporal Patterns
- Peak Hours: Distribution of trip start times
- Weekly Patterns: Activity levels by day of week
- Seasonal Trends: Monthly variations in effort and fleet size
Regional Comparison
Regional Metrics
- Fleet Size: Number of unique vessels per region
- Effort Distribution: Percentage of fishing time in each zone
- Operational Characteristics: Average trip duration and range
- Conflict Intensity: Average vessels per conflict event
Zone Preferences
Percentage of fishing time spent in each distance zone, revealing regional fishing strategies and resource utilization patterns.
Data Quality & Limitations
GPS Accuracy
- Position accuracy: ±5-10 meters
- Temporal resolution: 5-minute intervals
- Missing data: Gaps may occur due to signal loss or device malfunction
Analysis Assumptions
- Speed thresholds for activity classification based on typical SSF vessel capabilities
- Grid cell size balances spatial resolution with privacy concerns
- Conflict analysis assumes co-location indicates resource competition
Interpretation Caveats
- Gear type inference is probabilistic, not definitive
- Hotspots may reflect both resource abundance and accessibility
- VAC calculations assume consistent vessel identification across trips